Agricultural crops are often grown on farms which are too large for an individual to continuously monitor. As such, methods and systems for monitoring crops have been developed to reduce the chances of crop damage to some portion of a field due to, e.g., too little or too much water or fertilizer. Furthermore, it is important to discover damaging insects, fungi, or other blight as soon as possible, to prevent their spread to the rest of the field.
In many cases, crop damage affects the reflectance of leaves. As described in Gregory A. Carter, Responses of Leaf Spectral Reflectance to Plant Stress, 80(3) American Journal of Botany 239-243 (1993), not only does the net reflectance change, but the spectrum of reflectance also changes. In particular, based on spectral reflectance regression analysis, and vegetation index analysis including but not limited to the Normalized Difference Vegetation Index NDVI=(NIR−R)/(NIR+R), where NIR is the near infrared reflectance and R is the red reflectance, it is often possible to determine not only whether a plant is stressed, but in fact what type of stress that plant is undergoing. For example, different spectra are reflected from leaves suffering from competition, herbicide, a pathogen, ozone, insufficient mycorrhizae, barrier island environment, senescence, and dehydration. An excess of fertilization could cause a different NDVI ratio compared to an absence of water, both of which are different from a healthy plant. Nitrogen sufficiency is strongly correlated with particular, readily identifiable vegetation indices including but not limited to the Normalized Difference Vegetation Index (NDVI), the Green Normalized Vegetation Index (GNDVI), the Green Ratio Vegetation Index (GRVI), and the Normalized Green (NG) index.
Indicators of overall plant health in a region have been measured for several decades through the Landsat satellite program. Landsat images can provide information regarding regional drought, insect infestations, or good health. The satellites that provide Landsat images have a return frequency of 16 days, but the relevant reflectance data can only be captured if the satellite happens to be over a particular field during daylight hours, without significant cloud cover. As such, there can often be a relatively long time period (weeks) between opportunities to measure the reflectance of a particular field. Generally it is desirable to discover abnormal reflectance very quickly to avoid giving insects, fungi, or other undesirable contamination a chance to establish themselves in the field.
Furthermore, Landsat images do not provide high enough resolution to identify some types of blight or insect damage until they spread across a field and effect large areas. Generally, it is desirable to discover abnormal results in very small areas, even down to a leaf-by-leaf analysis, so that diseases with a known spectral profile can be identified and addressed before they spread to the point where a Landsat satellite would detect them.
In recent years, agricultural unmanned aerial vehicles (UAVs) have been used to acquire some data on crop growth and health. Satellites have periodic, fixed opportunities to image a given location, and those opportunities may be rendered useless due to cloud cover. UAVs, by contrast, can collect data on demand whenever conditions permit, resulting in more timely access to critical information. UAVs have their own challenges, however, including susceptibility to wind gusts. Furthermore, tasks such as aligning images between multiple cameras, computing vegetation indices, and stitching RGB, Near Infrared, and Vegetation Index Images into mosaics, can be computationally expensive. For this reason these activities are often performed in post-processing. Often a UAV must make multiple passes through a field in order to acquire RGB and Near Infrared images and GPS coordinates, then combine these results later, slowing the process and providing low resolution, as well as potentially introducing errors into the acquired data.